Real-Time Social Media Sentiment Analysis Using Big Data Architectures

Dr. Dhandapani Paramasivam; Shaik Shahul; S.Abhishek Kumar Reddy; P Thejaswee

Real-Time Social Media Sentiment Analysis Using Big Data Architectures

Authors: Dr. Dhandapani Paramasivam, Shaik Shahul, S.Abhishek Kumar Reddy, P Thejaswee

Unique Paper ID: 176008
Volume: 11
Issue: 11
PageNo: 4756-4765

Keywords: Big Data Sentiment Analysis Social Media Real-Time Analytics Apache Kafka Apache Spark Natural Language Processing (NLP).

Abstract:
The rapid expansion of social media platforms has made real-time sentiment analysis an essential tool for organizations, policymakers, and academic researchers. This project emphasizes the design and implementation of a Big Data architecture tailored for real-time sentiment analysis of social media content. By utilizing advanced data processing frameworks and machine learning techniques, the system processes vast volumes of unstructured textual data, delivering actionable insights into public sentiment and identifying emerging patterns. The proposed architecture incorporates Big Data tools such as Apache Kafka, Apache Spark, and Hadoop to support the seamless ingestion, processing, and storage of real-time data streams. Sentiment analysis is achieved using natural language processing (NLP) methods, enabling the classification of social media content into positive, negative, or neutral sentiments. The system’s architecture is designed with scalability and flexibility in mind, making it adaptable for sentiment monitoring across multiple platforms. This project provides a meaningful contribution to the areas of data analytics and social media monitoring by offering a reliable, real-time sentiment analysis solution. The system’s versatility makes it applicable to a range of domains, including marketing, brand management, and crisis communication.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4756-4765

Real-Time Social Media Sentiment Analysis Using Big Data Architectures

Available:https://ijirt.org/Article?manuscript=176008

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